ArticleProceedings of the National Academy of Sciences of the United States of America2021
Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- Analysis of serum NMR metabolomics in fibrotic diseases of the myocardium.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.NPJ precision oncology · 2026Article
- Metabolomic Relationships Between Lung Cancer and Alzheimer's Disease Using Serum Nuclear Magnetic Resonance Spectroscopy.NMR in biomedicine · 2026Article
- Biomarker discovery for lung adenocarcinoma diagnosis using liquid chromatography-mass spectrometry-based enhanced pseudotargeted metabolomics.Analytical and bioanalytical chemistry · 2026Article
- Early Detection of Lung Cancer: A Review of Innovative Milestones and Techniques.Journal of clinical medicine · 2025Review
- Advancing Non-Small-Cell Lung Cancer Management Through Multi-Omics Integration: Insights from Genomics, Metabolomics, and Radiomics.Diagnostics (Basel, Switzerland) · 2025Review
- Leveraging sequences missing from the human genome to diagnose cancer.Communications medicine · 2025Article
- Updates on lung cancer screening for early detection.The Korean journal of internal medicine · 2025Review
- Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large-Scale Multicenter Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.Frontiers in oncology · 2025Article
- Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling.Cancers · 2024Article
- Salivary metabolomic biomarkers for non-invasive lung cancer detection.Cancer science · 2024Article
- Association of metabolomics with PD-1 inhibitor plus chemotherapy outcomes in patients with advanced non-small-cell lung cancer.Journal for immunotherapy of cancer · 2024Article
- Best Practices in NMR Metabolomics: Current State.Trends in analytical chemistry : TRAC · 2024Article
- Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.International journal of medical sciences · 2024Review
- Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer.Cancer medicine · 2023Article
- How do prolonged anchorage-free lifetimes strengthen non-small-cell lung cancer cells to evade anoikis? - A link with altered cellular metabolomics.Biological research · 2023Article
- Functional Imaging of Hypoxia: PET and MRI.Cancers · 2023Review
- Designing a quality assurance process for quality control of nuclear magnetic resonance metabolomics studies of human blood.NMR in biomedicine · 2023Article
- Small molecule metabolites: discovery of biomarkers and therapeutic targets.Signal transduction and targeted therapy · 2023Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.